• The Journal of Korea Robotics Society
• /
• v.9 no.4
• /
• pp.232-241
• /
• 2014

This paper proposes an underwater localization algorithm using probabilistic object recognition. It is organized as follows; 1) recognizing artificial objects using imaging sonar, and 2) localizing the recognized objects and the vehicle using EKF(Extended Kalman Filter) based SLAM. For this purpose, we develop artificial landmarks to be recognized even under the unstable sonar images induced by noise. Moreover, a probabilistic recognition framework is proposed. In this way, the distance and bearing of the recognized artificial landmarks are acquired to perform the localization of the underwater vehicle. Using the recognized objects, EKF-based SLAM is carried out and results in a path of the underwater vehicle and the location of landmarks. The proposed localization algorithm is verified by experiments in a basin.

• International Journal of Internet, Broadcasting and Communication
• /
• v.13 no.2
• /
• pp.7-13
• /
• 2021

Machine vision is a technology that helps the computer as if a person recognizes and determines things. In recent years, as advanced technologies such as optical systems, artificial intelligence and big data advanced in conventional machine vision system became more accurate quality inspection and it increases the manufacturing efficiency. In machine vision systems using deep learning, the image quality of the input image is very important. However, most images obtained in the industrial field for quality inspection typically contain noise. This noise is a major factor in the performance of the machine vision system. Therefore, in order to improve the performance of the machine vision system, it is necessary to eliminate the noise of the image. There are lots of research being done to remove noise from the image. In this paper, we propose an autoencoder based machine vision system to eliminate noise in the image. Through experiment proposed model showed better performance compared to the basic autoencoder model in denoising and image reconstruction capability for MNIST and fashion MNIST data sets.

• The Journal of the Acoustical Society of Korea
• /
• v.38 no.6
• /
• pp.687-696
• /
• 2019

Conventional feature of seismic classification focuses on strong seismic classification, while it is not suitable for classifying micro-seismic waves. We propose a feature extraction method based on histogram and Principal Component Analysis (PCA) in frequency-time space suitable for classifying seismic waves including strong, micro, and artificial seismic waves, as well as noise classification. The proposed method essentially employs histogram and PCA based features by concatenating the frequency and time information for binary classification which consist strong-micro-artificial/noise and micro/noise and micro/artificial seismic waves. Based on the recent earthquake data from 2017 to 2018, effectiveness of the proposed feature extraction method is demonstrated by comparing it with existing methods.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.36 no.4
• /
• pp.233-242
• /
• 2023

The unscented Kalman filter (UKF), which is widely used to estimate the states of nonlinear dynamic systems, can be improved to realize robust system identification by using multiple sigma-point sets. When using Kalman filter methods for system identification, artificial noises must be appropriately selected to achieve optimal estimation performance. Additionally, an appropriate scaling factor for the sigma-points must be selected to capture the nonlinearity of the state-space model. This study entailed the use of Bouc-Wen hysteresis model to examine the nonlinear behavior of a single-degree-of-freedom oscillator. On the basis of the effects of the selected artificial noises and scaling factor, a new UKF method using multiple sigma-point sets was devised for improved robustness of the estimation over various signal-to-noise-ratio values. The results demonstrate that the proposed method can accurately track nonlinear system states even when the measurement noise levels are high, while being robust to the selection of artificial noise levels.

• Journal of KSNVE
• /
• v.8 no.2
• /
• pp.239-250
• /
• 1998

Traditionally, intake noise from internal combustion engine has not recevied much attention compared to exhaust noise. But nowadays, intake noise is a major contributing factor to automotive passenger compartment noise levels. The main objective of this paper is to identify the mechanism of generation, propagation and radiation of the intake noise. With a simplest geometric model, one of the main noise sources for the intake stroke is found to be the pressure surge, which is generated after intake valve closing. The pressure surge, which has the nonlinear acoustic behavior, propagates and radiates with relatively large amplitude. In this paper, unsteady compressible Navier-Stokes equations are employed for the intake stroke of axisymmetric model having a single moving cylinder and a single moving intake valve. To simulate the periodic motion of the piston and the valve, unsteady deforming mesh algorithm is employed and Thompson's non-reflecting boundary condition is applied to the radiation field. In order to resolve the small amplitude waves at the radiation field, essentially non-oscillatory(ENO) schemes with an artificial compression method (ACM) are used.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2005.11a
• /
• pp.427-430
• /
• 2005

The quality of various noises generated in the refrigerator is one of the important factors in deciding quality of the product. The main focus of sound control design has been shifted from reduction of sound level to improvement of sound duality for customer's preference. Up to date the purpose of noise control is the minimization of noise level. However despite of gradual decrease of noise level, occasionally the perceptional quality of noise has not been improved. In this paper, the relation between subjective and objective evaluation of sound quality has established and sound quality index is developed using ANN for evaluation of refrigerator's noise of both the starting noise and the stable running noise of compressor. To verify the usefulness of the index, the results in this paper have been compared with those surveyed by Consumer Union in USA.

• Journal of architectural history
• /
• v.31 no.1
• /
• pp.41-50
• /
• 2022

Traditional wooden buildings deform over time and are vulnerable to fire or earthquakes. Therefore, traditional wooden buildings require continuous management and repair, and securing architectural drawings is essential for repair and restoration. Unlike modernized CAD drawings, traditional wooden building drawings scan and store hand-drawn drawings, and in this process, many noise is included due to damage to the drawing itself. These drawings are digitized, but their utilization is poor due to noise. Difficulties in systematic management of traditional wooden buildings are increasing. Noise removal by existing algorithms has limited drawings that can be applied according to noise characteristics and the performance is not uniform. This study presents deep artificial neural network based noised reduction for architectural drawings. Front/side elevation drawings, floor plans, detail drawings of Korean wooden treasure buildings were considered. First, the noise properties of the architectural drawings were learned with both a cycle generative model and heuristic image fusion methods. Consequently, a noise reduction network was trained through supervised learning using training sets prepared using the noise models. The proposed method provided effective removal of noise without deteriorating fine lines in the architectural drawings and it showed good performance for various noise types.

• Proceedings of the KOSOMBE Conference
• /
• v.1996 no.11
• /
• pp.277-280
• /
• 1996

We have developed a ground-isolation circuit in order to reduce the noise of the internal controller system for the total artificial heart(TAH) and ventricular assist device(VAD). Using the ground-isolation technique, we could transmit the analog target signal to other pheriperal device including IBM PC via RS232C and polygraph, with no noise. Experimental results of VAD showed that there was less impulsive noise in current signal which caused in our previous conventional system. Therefore it could be proved that implementation of isolation technique is very effective to improve the signal to noise ratios of analog signal transmission for TAH or VAD.

• Journal of the Korean Society of Systems Engineering
• /
• v.19 no.2
• /
• pp.126-134
• /
• 2023

Drones are emerging as a new security threat, and the world is working to reduce them. Detection and identification are the most difficult and important parts of the anti-drone systems. Existing detection and identification methods each have their strengths and weaknesses, so complementary operations are required. Detection and identification performance in anti-drone systems can be improved through the use of artificial intelligence. This is because artificial intelligence can quickly analyze differences smaller than humans. There are three ways to utilize artificial intelligence. Through reinforcement learning-based physical control, noise and blur generated when the optical camera tracks the drone may be reduced, and tracking stability may be improved. The latest NeRF algorithm can be used to solve the problem of lack of enemy drone data. It is necessary to build a data network to utilize artificial intelligence. Through this, data can be efficiently collected and managed. In addition, model performance can be improved by regularly generating artificial intelligence learning data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.10a
• /
• pp.404-406
• /
• 2021

In modern society, artificial intelligence and automation are being applied in various fields due to the development of the 4th industrial revolution and IoT technology. In particular, systems with a high proportion of image processing, such as automated processes, intelligent CCTV, medical industry, robots, and drones, are susceptible to external factors noise. In this paper, we propose a digital filter based on noise estimation and weights to reconstruct an image in a complex noise environment. The proposed algorithm classifies the types of noise using noise judgment, and determines the noise level of the filtering mask to switch the filtering process to obtain the final output. In order to verify the performance of the proposed algorithm, simulation was conducted, compared with the existing filter algorithm, and the results were analyzed.